When consensus meets self-stabilization

Dolev, Shlomi; Kat, Ronen I.; Schiller, Elad Michael

doi:10.1016/j.jcss.2010.05.005

Cited by 24 publications

(22 citation statements)

References 33 publications

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“…There are practically-self-stabilizing algorithms for solving agreement [14,4], state-machine replication [4,12], and shared memory emulation [5]. None of them considers the studied problem.…”

Section: Related Workmentioning

confidence: 99%

“…, c x−1 , a x−1 is equal to x, which we denote by |R| = x. Let M AXIN T be an integer that is considered as a practically infinite [14] quantity for a system S (e.g., the system's lifetime). For example, M AXIN T can refer to 2 b (where b = 64 or larger) sequential system steps (e.g., single send or receive events).…”

Section: Execution Operators: Concatenation • and Segmentmentioning

confidence: 99%

“…Pseudo-self-stabilization [6] deals with the above inability by bounding the number of times in which the system violates safety. We consider the newer criteria of practically-self-stabilizing systems [2,14,4,12] that can address additional challenges. For example, any transient fault can cause a bounded counter to reach its maximum value and yet the system might need to increment the counter for an unbounded number of times after that overflow event.…”

Section: Introductionmentioning

confidence: 99%

“…Without fair scheduling, a system that takes an extraordinary (or even an infinite) number of steps is bound to break any ordering constraint, because unfair schedulers can arbitrarily suspend node operations and defer message arrivals until such violations occur. Having practical systems in mind, we consider this number of (sequential) steps to be no more than practically infinite [14,12], say, 2 b (where b = 64 or an even a larger integer, as long as a constant number of bits can represent it). Practically-self-stabilizing systems [2,4,12] require a bounded number of safety violations during any practically infinite period of the system run.…”

Section: Introductionmentioning

confidence: 99%

“…We refer to the latter as a wait-free recovery from transient faults. We note that the concept of practically-self-stabilizing systems is named by the concept of practically infinite executions [14].…”

Section: Introductionmentioning

confidence: 99%

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Practically-Self-stabilizing Vector Clocks in the Absence of Execution Fairness

Salem

Schiller

2019

Networked Systems

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Vector clock algorithms are basic wait-free building blocks that facilitate causal ordering of events. As wait-free algorithms, they are guaranteed to complete their operations within a finite number of steps. Stabilizing algorithms allow the system to recover after the occurrence of transient faults, such as soft errors and arbitrary violations of the assumptions according to which the system was designed to behave. We present the first, to the best of our knowledge, stabilizing vector clock algorithm for asynchronous crash-prone message-passing systems that can recover in a wait-free manner after the occurrence of transient faults. In these settings, it is challenging to demonstrate a finite and wait-free recovery from (communication and crash failures as well as) transient faults, bound the message and storage sizes, deal with the removal of all stale information without blocking, and deal with counter overflow events (which occur at different network nodes concurrently).We present an algorithm that never violates safety in the absence of transient faults and provides bounded time recovery during fair executions that follow the last transient fault. The novelty is that in the absence of execution fairness, the algorithm guarantees a bound on the number of times in which the system might violate safety (while existing algorithms might block forever due to the presence of both transient faults and crash failures).Since vector clocks facilitate a number of elementary synchronization building blocks (without requiring remote replica synchronization) in asynchronous systems, we believe that our analytical insights are useful for the design of other systems that cannot guarantee execution fairness. Context and Motivation.Vector clocks allow reasoning about causality among events in distributed systems, for example, when constructing distributed snapshots [17]. Shapiro et al. [24] showed that vector clocks are building blocks of several conflict-free replicated data types (CRDTs). CRDTs are distributed data structures that can be shared among many replicas in asynchronous networks. All replica updates occur independently and achieve strong eventual consistency without using mechanisms for synchronization [25] or roll-back.The industrial use of CRDTs includes globally distributed databases, such as the ones of Redis, Riak, Bet365, SoundCloud, TomTom, Phoenix, and Facebook. Some of these databases have around ten million concurrent users, ten thousand messages per second, store large volumes of data, and offer very low latency. However, while both the literature and the users demonstrate that large-scale decentralized systems can benefit from the use of CRDTs in general and vector clocks in particular, the relationship between fault-tolerance and strong eventual consistency has not received sufficient attention. Providing higher robustness degrees to CRDTs is nevertheless imperative for ensuring the availability and safety of these systems.Providing robustness in the presence of unexpected failures, i.e., the ones that...

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Section: Related Workmentioning

confidence: 99%

Section: Execution Operators: Concatenation • and Segmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Practically-Self-stabilizing Vector Clocks in the Absence of Execution Fairness

Salem

Schiller

2019

Networked Systems

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A Multiplier Effect Model for Price Stabilization Networks

Kiniwa¹,

Sandoh

2019

Complex Networks and Their Applications VIII

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Self-stabilizing Byzantine Fault-Tolerant Repeated Reliable Broadcast

Duvignau

Raynal

Schiller

2022

Lecture Notes in Computer Science

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Consensus, abstracting a myriad of problems in which processes have to agree on a single value, is one of the most celebrated problems of fault-tolerant distributed computing. Consensus applications include fundamental services for the environments of the Cloud and Blockchain, and in such challenging environments, malicious behaviors are often modeled as adversarial Byzantine faults.At OPODIS 2010, Mostéfaoui and Raynal (in short MR) presented a Byzantinetolerant solution to consensus in which the decided value cannot be a value proposed only by Byzantine processes. MR has optimal resilience coping with up to t < n/3 Byzantine nodes over n processes. MR provides this multivalued consensus object (which accepts proposals taken from a finite set of values) assuming the availability of a single Binary consensus object (which accepts proposals taken from the set {0, 1}).This work, which focuses on multivalued consensus, aims at the design of an even more robust solution than MR. Our proposal expands MR's fault-model with self-stabilization, a vigorous notion of fault-tolerance. In addition to tolerating Byzantine, self-stabilizing systems can automatically recover after the occurrence of arbitrary transient-faults. These faults represent any violation of the assumptions according to which the system was designed to operate (provided that the algorithm code remains intact).To the best of our knowledge, we propose the first self-stabilizing solution for intrusion-tolerant multivalued consensus for asynchronous message-passing systems prone to Byzantine failures. Our solution has a O(t) stabilization time from arbitrary transient faults.

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When consensus meets self-stabilization

Cited by 24 publications

References 33 publications

Practically-Self-stabilizing Vector Clocks in the Absence of Execution Fairness

Practically-Self-stabilizing Vector Clocks in the Absence of Execution Fairness

A Multiplier Effect Model for Price Stabilization Networks

Self-stabilizing Byzantine Fault-Tolerant Repeated Reliable Broadcast

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